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    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 11: Risk Parity Portfolio Optimization with Risk Factors using Stepwise Regression\n",
    "\n",
    "## 1. Downloading the data:"
   ]
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     "text": [
      "[*********************100%***********************]  30 of 30 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "\n",
    "yf.pdr_override()\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "assets.sort()\n",
    "\n",
    "# Tickers of factors\n",
    "\n",
    "factors = ['MTUM', 'QUAL', 'VLUE', 'SIZE', 'USMV']\n",
    "factors.sort()\n",
    "\n",
    "tickers = assets + factors\n",
    "tickers.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(tickers, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = tickers"
   ]
  },
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MTUM</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>USMV</th>\n",
       "      <th>VLUE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.4735%</td>\n",
       "      <td>0.2672%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.6779%</td>\n",
       "      <td>0.1635%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.5267%</td>\n",
       "      <td>-1.1914%</td>\n",
       "      <td>-0.5380%</td>\n",
       "      <td>-0.6253%</td>\n",
       "      <td>-1.8277%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-2.2293%</td>\n",
       "      <td>-2.3798%</td>\n",
       "      <td>-1.7181%</td>\n",
       "      <td>-1.6215%</td>\n",
       "      <td>-2.1609%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-0.9548%</td>\n",
       "      <td>-1.1377%</td>\n",
       "      <td>-1.1978%</td>\n",
       "      <td>-1.0086%</td>\n",
       "      <td>-1.0873%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>0.6043%</td>\n",
       "      <td>0.1479%</td>\n",
       "      <td>-0.5898%</td>\n",
       "      <td>0.1491%</td>\n",
       "      <td>-0.6183%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MTUM     QUAL     SIZE     USMV     VLUE\n",
       "Date                                                   \n",
       "2016-01-05  0.4735%  0.2672%  0.0000%  0.6779%  0.1635%\n",
       "2016-01-06 -0.5267% -1.1914% -0.5380% -0.6253% -1.8277%\n",
       "2016-01-07 -2.2293% -2.3798% -1.7181% -1.6215% -2.1609%\n",
       "2016-01-08 -0.9548% -1.1377% -1.1978% -1.0086% -1.0873%\n",
       "2016-01-11  0.6043%  0.1479% -0.5898%  0.1491% -0.6183%"
      ]
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   "source": [
    "# Calculating returns\n",
    "\n",
    "X = data[factors].pct_change().dropna()\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Mean Variance Portfolios\n",
    "\n",
    "### 2.1 Estimating the loadings matrix.\n",
    "\n",
    "This part is just to visualize how Riskfolio-Lib calculates a loadings matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "        }</style><table id=\"T_2a008_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >const</th>        <th class=\"col_heading level0 col1\" >MTUM</th>        <th class=\"col_heading level0 col2\" >QUAL</th>        <th class=\"col_heading level0 col3\" >SIZE</th>        <th class=\"col_heading level0 col4\" >USMV</th>        <th class=\"col_heading level0 col5\" >VLUE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2a008_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_2a008_row0_col0\" class=\"data row0 col0\" >-0.0006</td>\n",
       "                        <td id=\"T_2a008_row0_col1\" class=\"data row0 col1\" >-0.6551</td>\n",
       "                        <td id=\"T_2a008_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row0_col3\" class=\"data row0 col3\" >0.9406</td>\n",
       "                        <td id=\"T_2a008_row0_col4\" class=\"data row0 col4\" >-0.7883</td>\n",
       "                        <td id=\"T_2a008_row0_col5\" class=\"data row0 col5\" >1.7237</td>\n",
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       "            <tr>\n",
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       "                        <td id=\"T_2a008_row1_col0\" class=\"data row1 col0\" >0.0005</td>\n",
       "                        <td id=\"T_2a008_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row1_col2\" class=\"data row1 col2\" >1.1744</td>\n",
       "                        <td id=\"T_2a008_row1_col3\" class=\"data row1 col3\" >0.3616</td>\n",
       "                        <td id=\"T_2a008_row1_col4\" class=\"data row1 col4\" >-0.4322</td>\n",
       "                        <td id=\"T_2a008_row1_col5\" class=\"data row1 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_2a008_row2_col0\" class=\"data row2 col0\" >0.0003</td>\n",
       "                        <td id=\"T_2a008_row2_col1\" class=\"data row2 col1\" >0.3146</td>\n",
       "                        <td id=\"T_2a008_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row2_col3\" class=\"data row2 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row2_col4\" class=\"data row2 col4\" >0.7717</td>\n",
       "                        <td id=\"T_2a008_row2_col5\" class=\"data row2 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_2a008_row3_col0\" class=\"data row3 col0\" >-0.0003</td>\n",
       "                        <td id=\"T_2a008_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row3_col2\" class=\"data row3 col2\" >0.8123</td>\n",
       "                        <td id=\"T_2a008_row3_col3\" class=\"data row3 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row3_col4\" class=\"data row3 col4\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row3_col5\" class=\"data row3 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_2a008_row4_col0\" class=\"data row4 col0\" >0.0001</td>\n",
       "                        <td id=\"T_2a008_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row4_col2\" class=\"data row4 col2\" >0.4958</td>\n",
       "                        <td id=\"T_2a008_row4_col3\" class=\"data row4 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row4_col4\" class=\"data row4 col4\" >0.4962</td>\n",
       "                        <td id=\"T_2a008_row4_col5\" class=\"data row4 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_2a008_row5_col0\" class=\"data row5 col0\" >0.0001</td>\n",
       "                        <td id=\"T_2a008_row5_col1\" class=\"data row5 col1\" >-0.5595</td>\n",
       "                        <td id=\"T_2a008_row5_col2\" class=\"data row5 col2\" >-0.2157</td>\n",
       "                        <td id=\"T_2a008_row5_col3\" class=\"data row5 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row5_col4\" class=\"data row5 col4\" >1.8341</td>\n",
       "                        <td id=\"T_2a008_row5_col5\" class=\"data row5 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_2a008_row6_col0\" class=\"data row6 col0\" >-0.0003</td>\n",
       "                        <td id=\"T_2a008_row6_col1\" class=\"data row6 col1\" >-0.4782</td>\n",
       "                        <td id=\"T_2a008_row6_col2\" class=\"data row6 col2\" >-0.5994</td>\n",
       "                        <td id=\"T_2a008_row6_col3\" class=\"data row6 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row6_col4\" class=\"data row6 col4\" >2.0793</td>\n",
       "                        <td id=\"T_2a008_row6_col5\" class=\"data row6 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_2a008_row7_col0\" class=\"data row7 col0\" >0.0004</td>\n",
       "                        <td id=\"T_2a008_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row7_col3\" class=\"data row7 col3\" >0.3631</td>\n",
       "                        <td id=\"T_2a008_row7_col4\" class=\"data row7 col4\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row7_col5\" class=\"data row7 col5\" >0.8090</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_2a008_row8_col0\" class=\"data row8 col0\" >0.0002</td>\n",
       "                        <td id=\"T_2a008_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row8_col3\" class=\"data row8 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row8_col4\" class=\"data row8 col4\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row8_col5\" class=\"data row8 col5\" >1.2514</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_2a008_row9_col0\" class=\"data row9 col0\" >0.0001</td>\n",
       "                        <td id=\"T_2a008_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row9_col2\" class=\"data row9 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row9_col3\" class=\"data row9 col3\" >0.3412</td>\n",
       "                        <td id=\"T_2a008_row9_col4\" class=\"data row9 col4\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row9_col5\" class=\"data row9 col5\" >0.5797</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_2a008_row10_col0\" class=\"data row10 col0\" >0.0005</td>\n",
       "                        <td id=\"T_2a008_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row10_col2\" class=\"data row10 col2\" >0.3077</td>\n",
       "                        <td id=\"T_2a008_row10_col3\" class=\"data row10 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row10_col4\" class=\"data row10 col4\" >-0.5485</td>\n",
       "                        <td id=\"T_2a008_row10_col5\" class=\"data row10 col5\" >1.1512</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_2a008_row11_col0\" class=\"data row11 col0\" >-0.0001</td>\n",
       "                        <td id=\"T_2a008_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row11_col3\" class=\"data row11 col3\" >0.3642</td>\n",
       "                        <td id=\"T_2a008_row11_col4\" class=\"data row11 col4\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row11_col5\" class=\"data row11 col5\" >0.6767</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_2a008_row12_col0\" class=\"data row12 col0\" >0.0003</td>\n",
       "                        <td id=\"T_2a008_row12_col1\" class=\"data row12 col1\" >-0.2693</td>\n",
       "                        <td id=\"T_2a008_row12_col2\" class=\"data row12 col2\" >0.6808</td>\n",
       "                        <td id=\"T_2a008_row12_col3\" class=\"data row12 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row12_col4\" class=\"data row12 col4\" >0.6066</td>\n",
       "                        <td id=\"T_2a008_row12_col5\" class=\"data row12 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_2a008_row13_col0\" class=\"data row13 col0\" >-0.0004</td>\n",
       "                        <td id=\"T_2a008_row13_col1\" class=\"data row13 col1\" >-0.4572</td>\n",
       "                        <td id=\"T_2a008_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row13_col3\" class=\"data row13 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row13_col4\" class=\"data row13 col4\" >1.4359</td>\n",
       "                        <td id=\"T_2a008_row13_col5\" class=\"data row13 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_2a008_row14_col0\" class=\"data row14 col0\" >0.0005</td>\n",
       "                        <td id=\"T_2a008_row14_col1\" class=\"data row14 col1\" >1.0302</td>\n",
       "                        <td id=\"T_2a008_row14_col2\" class=\"data row14 col2\" >0.6531</td>\n",
       "                        <td id=\"T_2a008_row14_col3\" class=\"data row14 col3\" >-0.2640</td>\n",
       "                        <td id=\"T_2a008_row14_col4\" class=\"data row14 col4\" >-0.2595</td>\n",
       "                        <td id=\"T_2a008_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_2a008_row15_col0\" class=\"data row15 col0\" >-0.0000</td>\n",
       "                        <td id=\"T_2a008_row15_col1\" class=\"data row15 col1\" >-0.4648</td>\n",
       "                        <td id=\"T_2a008_row15_col2\" class=\"data row15 col2\" >-0.4612</td>\n",
       "                        <td id=\"T_2a008_row15_col3\" class=\"data row15 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row15_col4\" class=\"data row15 col4\" >2.3257</td>\n",
       "                        <td id=\"T_2a008_row15_col5\" class=\"data row15 col5\" >-0.3532</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_2a008_row16_col0\" class=\"data row16 col0\" >0.0003</td>\n",
       "                        <td id=\"T_2a008_row16_col1\" class=\"data row16 col1\" >-0.4420</td>\n",
       "                        <td id=\"T_2a008_row16_col2\" class=\"data row16 col2\" >1.0319</td>\n",
       "                        <td id=\"T_2a008_row16_col3\" class=\"data row16 col3\" >0.2217</td>\n",
       "                        <td id=\"T_2a008_row16_col4\" class=\"data row16 col4\" >-0.4831</td>\n",
       "                        <td id=\"T_2a008_row16_col5\" class=\"data row16 col5\" >0.7536</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_2a008_row17_col0\" class=\"data row17 col0\" >-0.0004</td>\n",
       "                        <td id=\"T_2a008_row17_col1\" class=\"data row17 col1\" >-0.3311</td>\n",
       "                        <td id=\"T_2a008_row17_col2\" class=\"data row17 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row17_col3\" class=\"data row17 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row17_col4\" class=\"data row17 col4\" >1.7073</td>\n",
       "                        <td id=\"T_2a008_row17_col5\" class=\"data row17 col5\" >-0.4915</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_2a008_row18_col0\" class=\"data row18 col0\" >-0.0005</td>\n",
       "                        <td id=\"T_2a008_row18_col1\" class=\"data row18 col1\" >-0.3267</td>\n",
       "                        <td id=\"T_2a008_row18_col2\" class=\"data row18 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row18_col3\" class=\"data row18 col3\" >0.2595</td>\n",
       "                        <td id=\"T_2a008_row18_col4\" class=\"data row18 col4\" >0.6973</td>\n",
       "                        <td id=\"T_2a008_row18_col5\" class=\"data row18 col5\" >0.4801</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_2a008_row19_col0\" class=\"data row19 col0\" >-0.0000</td>\n",
       "                        <td id=\"T_2a008_row19_col1\" class=\"data row19 col1\" >-0.4521</td>\n",
       "                        <td id=\"T_2a008_row19_col2\" class=\"data row19 col2\" >-0.3719</td>\n",
       "                        <td id=\"T_2a008_row19_col3\" class=\"data row19 col3\" >-0.2951</td>\n",
       "                        <td id=\"T_2a008_row19_col4\" class=\"data row19 col4\" >1.3089</td>\n",
       "                        <td id=\"T_2a008_row19_col5\" class=\"data row19 col5\" >0.7620</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_2a008_row20_col0\" class=\"data row20 col0\" >0.0005</td>\n",
       "                        <td id=\"T_2a008_row20_col1\" class=\"data row20 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row20_col2\" class=\"data row20 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row20_col3\" class=\"data row20 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row20_col4\" class=\"data row20 col4\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row20_col5\" class=\"data row20 col5\" >0.7562</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_2a008_row21_col0\" class=\"data row21 col0\" >0.0002</td>\n",
       "                        <td id=\"T_2a008_row21_col1\" class=\"data row21 col1\" >0.2867</td>\n",
       "                        <td id=\"T_2a008_row21_col2\" class=\"data row21 col2\" >0.5400</td>\n",
       "                        <td id=\"T_2a008_row21_col3\" class=\"data row21 col3\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row21_col4\" class=\"data row21 col4\" >0.3864</td>\n",
       "                        <td id=\"T_2a008_row21_col5\" class=\"data row21 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_2a008_row22_col0\" class=\"data row22 col0\" >-0.0003</td>\n",
       "                        <td id=\"T_2a008_row22_col1\" class=\"data row22 col1\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row22_col2\" class=\"data row22 col2\" >0.4485</td>\n",
       "                        <td id=\"T_2a008_row22_col3\" class=\"data row22 col3\" >0.4429</td>\n",
       "                        <td id=\"T_2a008_row22_col4\" class=\"data row22 col4\" >-0.6363</td>\n",
       "                        <td id=\"T_2a008_row22_col5\" class=\"data row22 col5\" >0.8108</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_2a008_row23_col0\" class=\"data row23 col0\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row23_col1\" class=\"data row23 col1\" >-0.5099</td>\n",
       "                        <td id=\"T_2a008_row23_col2\" class=\"data row23 col2\" >-0.5271</td>\n",
       "                        <td id=\"T_2a008_row23_col3\" class=\"data row23 col3\" >-0.2886</td>\n",
       "                        <td id=\"T_2a008_row23_col4\" class=\"data row23 col4\" >1.8941</td>\n",
       "                        <td id=\"T_2a008_row23_col5\" class=\"data row23 col5\" >0.4321</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a008_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_2a008_row24_col0\" class=\"data row24 col0\" >0.0004</td>\n",
       "                        <td id=\"T_2a008_row24_col1\" class=\"data row24 col1\" >-0.2371</td>\n",
       "                        <td id=\"T_2a008_row24_col2\" class=\"data row24 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a008_row24_col3\" class=\"data row24 col3\" >0.3553</td>\n",
       "                        <td id=\"T_2a008_row24_col4\" class=\"data row24 col4\" >-0.8203</td>\n",
       "                        <td id=\"T_2a008_row24_col5\" class=\"data row24 col5\" >1.6431</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd2e47d77c0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import riskfolio.ParamsEstimation as pe\n",
    "\n",
    "step = 'Forward' # Could be Forward or Backward stepwise regression\n",
    "loadings = pe.loadings_matrix(X=X, Y=Y, stepwise=step)\n",
    "\n",
    "loadings.style.format(\"{:.4f}\").background_gradient(cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Calculating the portfolio that maximizes Sharpe ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.9509%</td>\n",
       "      <td>11.8691%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>9.7091%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.3628%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.4797%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.5105%</td>\n",
       "      <td>1.3012%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.0981%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 0.0000% 5.9509% 11.8691% 0.0000% 0.0000% 9.7091% 0.0000% 4.3628%   \n",
       "\n",
       "            HPQ     JCI  ...       NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 10.4797% 0.0000% 0.0000% 0.0000% 0.0000% 5.5105%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 1.3012% 0.0000% 4.0981% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.Portfolio as pf\n",
    "\n",
    "# Building the portfolio object\n",
    "port = pf.Portfolio(returns=Y)\n",
    "\n",
    "# Calculating optimum portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
    "\n",
    "port.factors = X\n",
    "port.factors_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model='FM' # Factor Model\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = False # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.PlotFunctions as plf\n",
    "\n",
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = plf.plot_pie(w=w, title='Sharpe FM Mean Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plotting Risk Contribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the risk composition of the portfolio\n",
    "\n",
    "ax = plf.plot_risk_con(w, cov=port.cov, returns=port.returns, rm=rm, rf=0, alpha=0.01,\n",
    "                       color=\"tab:blue\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Risk Parity Portfolios Using Risk Factors with Other Risk Measures\n",
    "\n",
    "In this part I will calculate risk parity portfolios using factors models. First I'm going to calculate risk parity portfolio when we use variance as risk measure, then I'm going to calculate the risk parity portfolios for all available risk measures.\n",
    "\n",
    "### 3.1 Calculating the risk parity portfolio for variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>2.3353%</td>\n",
       "      <td>3.1191%</td>\n",
       "      <td>3.9882%</td>\n",
       "      <td>4.0285%</td>\n",
       "      <td>4.0301%</td>\n",
       "      <td>5.3654%</td>\n",
       "      <td>5.2659%</td>\n",
       "      <td>3.0163%</td>\n",
       "      <td>2.7127%</td>\n",
       "      <td>3.7669%</td>\n",
       "      <td>...</td>\n",
       "      <td>6.5950%</td>\n",
       "      <td>3.0655%</td>\n",
       "      <td>6.6764%</td>\n",
       "      <td>3.6183%</td>\n",
       "      <td>4.7072%</td>\n",
       "      <td>3.8289%</td>\n",
       "      <td>3.3394%</td>\n",
       "      <td>3.0023%</td>\n",
       "      <td>5.4384%</td>\n",
       "      <td>3.0065%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA     BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 2.3353% 3.1191% 3.9882% 4.0285% 4.0301% 5.3654% 5.2659% 3.0163%   \n",
       "\n",
       "            HPQ     JCI  ...      NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 2.7127% 3.7669%  ... 6.5950% 3.0655% 6.6764% 3.6183% 4.7072% 3.8289%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 3.3394% 3.0023% 5.4384% 3.0065%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = None # Risk contribution constraints vector\n",
    "\n",
    "w_rp = port.rp_optimization(model=model, rm=rm, rf=rf, b=b, hist=hist)\n",
    "\n",
    "display(w_rp.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plf.plot_pie(w=w_rp, title='Risk Parity Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3  Plotting Risk Composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plf.plot_risk_con(w_rp, cov=port.cov_fm, returns=port.returns_fm, rm=rm, rf=0, alpha=0.01,\n",
    "                       color=\"tab:blue\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Calculate Optimal Portfolios for Several Risk Measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "# port.reset_linear_constraints() # To reset linear constraints (factor constraints)\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM',\n",
    "       'EVaR', 'CVaR', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "# When we use hist = True the risk measures all calculated\n",
    "# using historical returns, while when hist = False the\n",
    "# risk measures are calculated using the expected returns \n",
    "#  based on risk factor model: R = a + B * F\n",
    "\n",
    "hist = False\n",
    "\n",
    "for i in rms:\n",
    "    w = port.rp_optimization(model=model, rm=i, rf=rf, b=b, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "        }#T_dd161_row23_col6{\n",
       "            background-color:  #349a52;\n",
       "            color:  #000000;\n",
       "        }#T_dd161_row23_col7{\n",
       "            background-color:  #77c679;\n",
       "            color:  #000000;\n",
       "        }#T_dd161_row23_col9{\n",
       "            background-color:  #a1d889;\n",
       "            color:  #000000;\n",
       "        }#T_dd161_row24_col6{\n",
       "            background-color:  #f9fdc2;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_dd161_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MV</th>        <th class=\"col_heading level0 col1\" >MAD</th>        <th class=\"col_heading level0 col2\" >MSV</th>        <th class=\"col_heading level0 col3\" >FLPM</th>        <th class=\"col_heading level0 col4\" >SLPM</th>        <th class=\"col_heading level0 col5\" >EVaR</th>        <th class=\"col_heading level0 col6\" >CVaR</th>        <th class=\"col_heading level0 col7\" >CDaR</th>        <th class=\"col_heading level0 col8\" >UCI</th>        <th class=\"col_heading level0 col9\" >EDaR</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_dd161_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_dd161_row0_col0\" class=\"data row0 col0\" >2.34%</td>\n",
       "                        <td id=\"T_dd161_row0_col1\" class=\"data row0 col1\" >2.31%</td>\n",
       "                        <td id=\"T_dd161_row0_col2\" class=\"data row0 col2\" >2.38%</td>\n",
       "                        <td id=\"T_dd161_row0_col3\" class=\"data row0 col3\" >2.10%</td>\n",
       "                        <td id=\"T_dd161_row0_col4\" class=\"data row0 col4\" >2.26%</td>\n",
       "                        <td id=\"T_dd161_row0_col5\" class=\"data row0 col5\" >2.74%</td>\n",
       "                        <td id=\"T_dd161_row0_col6\" class=\"data row0 col6\" >2.27%</td>\n",
       "                        <td id=\"T_dd161_row0_col7\" class=\"data row0 col7\" >1.60%</td>\n",
       "                        <td id=\"T_dd161_row0_col8\" class=\"data row0 col8\" >1.50%</td>\n",
       "                        <td id=\"T_dd161_row0_col9\" class=\"data row0 col9\" >1.41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_dd161_row1_col0\" class=\"data row1 col0\" >3.12%</td>\n",
       "                        <td id=\"T_dd161_row1_col1\" class=\"data row1 col1\" >3.13%</td>\n",
       "                        <td id=\"T_dd161_row1_col2\" class=\"data row1 col2\" >2.98%</td>\n",
       "                        <td id=\"T_dd161_row1_col3\" class=\"data row1 col3\" >3.34%</td>\n",
       "                        <td id=\"T_dd161_row1_col4\" class=\"data row1 col4\" >3.05%</td>\n",
       "                        <td id=\"T_dd161_row1_col5\" class=\"data row1 col5\" >3.24%</td>\n",
       "                        <td id=\"T_dd161_row1_col6\" class=\"data row1 col6\" >2.88%</td>\n",
       "                        <td id=\"T_dd161_row1_col7\" class=\"data row1 col7\" >2.97%</td>\n",
       "                        <td id=\"T_dd161_row1_col8\" class=\"data row1 col8\" >3.43%</td>\n",
       "                        <td id=\"T_dd161_row1_col9\" class=\"data row1 col9\" >2.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_dd161_row2_col0\" class=\"data row2 col0\" >3.99%</td>\n",
       "                        <td id=\"T_dd161_row2_col1\" class=\"data row2 col1\" >3.98%</td>\n",
       "                        <td id=\"T_dd161_row2_col2\" class=\"data row2 col2\" >3.75%</td>\n",
       "                        <td id=\"T_dd161_row2_col3\" class=\"data row2 col3\" >4.26%</td>\n",
       "                        <td id=\"T_dd161_row2_col4\" class=\"data row2 col4\" >3.86%</td>\n",
       "                        <td id=\"T_dd161_row2_col5\" class=\"data row2 col5\" >3.73%</td>\n",
       "                        <td id=\"T_dd161_row2_col6\" class=\"data row2 col6\" >3.72%</td>\n",
       "                        <td id=\"T_dd161_row2_col7\" class=\"data row2 col7\" >4.30%</td>\n",
       "                        <td id=\"T_dd161_row2_col8\" class=\"data row2 col8\" >5.00%</td>\n",
       "                        <td id=\"T_dd161_row2_col9\" class=\"data row2 col9\" >3.72%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_dd161_row3_col0\" class=\"data row3 col0\" >4.03%</td>\n",
       "                        <td id=\"T_dd161_row3_col1\" class=\"data row3 col1\" >4.51%</td>\n",
       "                        <td id=\"T_dd161_row3_col2\" class=\"data row3 col2\" >4.30%</td>\n",
       "                        <td id=\"T_dd161_row3_col3\" class=\"data row3 col3\" >4.22%</td>\n",
       "                        <td id=\"T_dd161_row3_col4\" class=\"data row3 col4\" >4.16%</td>\n",
       "                        <td id=\"T_dd161_row3_col5\" class=\"data row3 col5\" >4.40%</td>\n",
       "                        <td id=\"T_dd161_row3_col6\" class=\"data row3 col6\" >4.03%</td>\n",
       "                        <td id=\"T_dd161_row3_col7\" class=\"data row3 col7\" >3.17%</td>\n",
       "                        <td id=\"T_dd161_row3_col8\" class=\"data row3 col8\" >3.04%</td>\n",
       "                        <td id=\"T_dd161_row3_col9\" class=\"data row3 col9\" >2.78%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_dd161_row4_col0\" class=\"data row4 col0\" >4.03%</td>\n",
       "                        <td id=\"T_dd161_row4_col1\" class=\"data row4 col1\" >4.10%</td>\n",
       "                        <td id=\"T_dd161_row4_col2\" class=\"data row4 col2\" >3.94%</td>\n",
       "                        <td id=\"T_dd161_row4_col3\" class=\"data row4 col3\" >4.17%</td>\n",
       "                        <td id=\"T_dd161_row4_col4\" class=\"data row4 col4\" >3.96%</td>\n",
       "                        <td id=\"T_dd161_row4_col5\" class=\"data row4 col5\" >3.98%</td>\n",
       "                        <td id=\"T_dd161_row4_col6\" class=\"data row4 col6\" >3.83%</td>\n",
       "                        <td id=\"T_dd161_row4_col7\" class=\"data row4 col7\" >3.73%</td>\n",
       "                        <td id=\"T_dd161_row4_col8\" class=\"data row4 col8\" >3.90%</td>\n",
       "                        <td id=\"T_dd161_row4_col9\" class=\"data row4 col9\" >3.27%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_dd161_row5_col0\" class=\"data row5 col0\" >5.37%</td>\n",
       "                        <td id=\"T_dd161_row5_col1\" class=\"data row5 col1\" >5.08%</td>\n",
       "                        <td id=\"T_dd161_row5_col2\" class=\"data row5 col2\" >5.31%</td>\n",
       "                        <td id=\"T_dd161_row5_col3\" class=\"data row5 col3\" >5.31%</td>\n",
       "                        <td id=\"T_dd161_row5_col4\" class=\"data row5 col4\" >5.46%</td>\n",
       "                        <td id=\"T_dd161_row5_col5\" class=\"data row5 col5\" >4.98%</td>\n",
       "                        <td id=\"T_dd161_row5_col6\" class=\"data row5 col6\" >5.60%</td>\n",
       "                        <td id=\"T_dd161_row5_col7\" class=\"data row5 col7\" >6.81%</td>\n",
       "                        <td id=\"T_dd161_row5_col8\" class=\"data row5 col8\" >6.63%</td>\n",
       "                        <td id=\"T_dd161_row5_col9\" class=\"data row5 col9\" >7.35%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_dd161_row6_col0\" class=\"data row6 col0\" >5.27%</td>\n",
       "                        <td id=\"T_dd161_row6_col1\" class=\"data row6 col1\" >5.99%</td>\n",
       "                        <td id=\"T_dd161_row6_col2\" class=\"data row6 col2\" >6.34%</td>\n",
       "                        <td id=\"T_dd161_row6_col3\" class=\"data row6 col3\" >5.62%</td>\n",
       "                        <td id=\"T_dd161_row6_col4\" class=\"data row6 col4\" >6.24%</td>\n",
       "                        <td id=\"T_dd161_row6_col5\" class=\"data row6 col5\" >5.57%</td>\n",
       "                        <td id=\"T_dd161_row6_col6\" class=\"data row6 col6\" >6.65%</td>\n",
       "                        <td id=\"T_dd161_row6_col7\" class=\"data row6 col7\" >6.34%</td>\n",
       "                        <td id=\"T_dd161_row6_col8\" class=\"data row6 col8\" >5.23%</td>\n",
       "                        <td id=\"T_dd161_row6_col9\" class=\"data row6 col9\" >7.34%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_dd161_row7_col0\" class=\"data row7 col0\" >3.02%</td>\n",
       "                        <td id=\"T_dd161_row7_col1\" class=\"data row7 col1\" >2.91%</td>\n",
       "                        <td id=\"T_dd161_row7_col2\" class=\"data row7 col2\" >2.86%</td>\n",
       "                        <td id=\"T_dd161_row7_col3\" class=\"data row7 col3\" >3.05%</td>\n",
       "                        <td id=\"T_dd161_row7_col4\" class=\"data row7 col4\" >2.91%</td>\n",
       "                        <td id=\"T_dd161_row7_col5\" class=\"data row7 col5\" >3.13%</td>\n",
       "                        <td id=\"T_dd161_row7_col6\" class=\"data row7 col6\" >2.82%</td>\n",
       "                        <td id=\"T_dd161_row7_col7\" class=\"data row7 col7\" >2.88%</td>\n",
       "                        <td id=\"T_dd161_row7_col8\" class=\"data row7 col8\" >3.27%</td>\n",
       "                        <td id=\"T_dd161_row7_col9\" class=\"data row7 col9\" >2.44%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_dd161_row8_col0\" class=\"data row8 col0\" >2.71%</td>\n",
       "                        <td id=\"T_dd161_row8_col1\" class=\"data row8 col1\" >2.58%</td>\n",
       "                        <td id=\"T_dd161_row8_col2\" class=\"data row8 col2\" >2.55%</td>\n",
       "                        <td id=\"T_dd161_row8_col3\" class=\"data row8 col3\" >2.61%</td>\n",
       "                        <td id=\"T_dd161_row8_col4\" class=\"data row8 col4\" >2.55%</td>\n",
       "                        <td id=\"T_dd161_row8_col5\" class=\"data row8 col5\" >2.77%</td>\n",
       "                        <td id=\"T_dd161_row8_col6\" class=\"data row8 col6\" >2.50%</td>\n",
       "                        <td id=\"T_dd161_row8_col7\" class=\"data row8 col7\" >2.26%</td>\n",
       "                        <td id=\"T_dd161_row8_col8\" class=\"data row8 col8\" >2.41%</td>\n",
       "                        <td id=\"T_dd161_row8_col9\" class=\"data row8 col9\" >1.97%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_dd161_row9_col0\" class=\"data row9 col0\" >3.77%</td>\n",
       "                        <td id=\"T_dd161_row9_col1\" class=\"data row9 col1\" >3.74%</td>\n",
       "                        <td id=\"T_dd161_row9_col2\" class=\"data row9 col2\" >3.67%</td>\n",
       "                        <td id=\"T_dd161_row9_col3\" class=\"data row9 col3\" >3.71%</td>\n",
       "                        <td id=\"T_dd161_row9_col4\" class=\"data row9 col4\" >3.65%</td>\n",
       "                        <td id=\"T_dd161_row9_col5\" class=\"data row9 col5\" >3.97%</td>\n",
       "                        <td id=\"T_dd161_row9_col6\" class=\"data row9 col6\" >3.57%</td>\n",
       "                        <td id=\"T_dd161_row9_col7\" class=\"data row9 col7\" >3.21%</td>\n",
       "                        <td id=\"T_dd161_row9_col8\" class=\"data row9 col8\" >3.32%</td>\n",
       "                        <td id=\"T_dd161_row9_col9\" class=\"data row9 col9\" >2.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_dd161_row10_col0\" class=\"data row10 col0\" >3.37%</td>\n",
       "                        <td id=\"T_dd161_row10_col1\" class=\"data row10 col1\" >3.13%</td>\n",
       "                        <td id=\"T_dd161_row10_col2\" class=\"data row10 col2\" >3.08%</td>\n",
       "                        <td id=\"T_dd161_row10_col3\" class=\"data row10 col3\" >3.30%</td>\n",
       "                        <td id=\"T_dd161_row10_col4\" class=\"data row10 col4\" >3.13%</td>\n",
       "                        <td id=\"T_dd161_row10_col5\" class=\"data row10 col5\" >3.49%</td>\n",
       "                        <td id=\"T_dd161_row10_col6\" class=\"data row10 col6\" >3.03%</td>\n",
       "                        <td id=\"T_dd161_row10_col7\" class=\"data row10 col7\" >2.93%</td>\n",
       "                        <td id=\"T_dd161_row10_col8\" class=\"data row10 col8\" >3.43%</td>\n",
       "                        <td id=\"T_dd161_row10_col9\" class=\"data row10 col9\" >2.46%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_dd161_row11_col0\" class=\"data row11 col0\" >3.30%</td>\n",
       "                        <td id=\"T_dd161_row11_col1\" class=\"data row11 col1\" >3.29%</td>\n",
       "                        <td id=\"T_dd161_row11_col2\" class=\"data row11 col2\" >3.24%</td>\n",
       "                        <td id=\"T_dd161_row11_col3\" class=\"data row11 col3\" >3.19%</td>\n",
       "                        <td id=\"T_dd161_row11_col4\" class=\"data row11 col4\" >3.18%</td>\n",
       "                        <td id=\"T_dd161_row11_col5\" class=\"data row11 col5\" >3.48%</td>\n",
       "                        <td id=\"T_dd161_row11_col6\" class=\"data row11 col6\" >3.13%</td>\n",
       "                        <td id=\"T_dd161_row11_col7\" class=\"data row11 col7\" >2.64%</td>\n",
       "                        <td id=\"T_dd161_row11_col8\" class=\"data row11 col8\" >2.64%</td>\n",
       "                        <td id=\"T_dd161_row11_col9\" class=\"data row11 col9\" >2.30%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_dd161_row12_col0\" class=\"data row12 col0\" >4.31%</td>\n",
       "                        <td id=\"T_dd161_row12_col1\" class=\"data row12 col1\" >4.03%</td>\n",
       "                        <td id=\"T_dd161_row12_col2\" class=\"data row12 col2\" >3.95%</td>\n",
       "                        <td id=\"T_dd161_row12_col3\" class=\"data row12 col3\" >4.29%</td>\n",
       "                        <td id=\"T_dd161_row12_col4\" class=\"data row12 col4\" >4.05%</td>\n",
       "                        <td id=\"T_dd161_row12_col5\" class=\"data row12 col5\" >4.05%</td>\n",
       "                        <td id=\"T_dd161_row12_col6\" class=\"data row12 col6\" >3.92%</td>\n",
       "                        <td id=\"T_dd161_row12_col7\" class=\"data row12 col7\" >4.25%</td>\n",
       "                        <td id=\"T_dd161_row12_col8\" class=\"data row12 col8\" >4.70%</td>\n",
       "                        <td id=\"T_dd161_row12_col9\" class=\"data row12 col9\" >3.74%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_dd161_row13_col0\" class=\"data row13 col0\" >5.03%</td>\n",
       "                        <td id=\"T_dd161_row13_col1\" class=\"data row13 col1\" >5.13%</td>\n",
       "                        <td id=\"T_dd161_row13_col2\" class=\"data row13 col2\" >5.26%</td>\n",
       "                        <td id=\"T_dd161_row13_col3\" class=\"data row13 col3\" >4.74%</td>\n",
       "                        <td id=\"T_dd161_row13_col4\" class=\"data row13 col4\" >5.10%</td>\n",
       "                        <td id=\"T_dd161_row13_col5\" class=\"data row13 col5\" >4.93%</td>\n",
       "                        <td id=\"T_dd161_row13_col6\" class=\"data row13 col6\" >5.24%</td>\n",
       "                        <td id=\"T_dd161_row13_col7\" class=\"data row13 col7\" >4.35%</td>\n",
       "                        <td id=\"T_dd161_row13_col8\" class=\"data row13 col8\" >3.79%</td>\n",
       "                        <td id=\"T_dd161_row13_col9\" class=\"data row13 col9\" >4.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_dd161_row14_col0\" class=\"data row14 col0\" >3.09%</td>\n",
       "                        <td id=\"T_dd161_row14_col1\" class=\"data row14 col1\" >3.10%</td>\n",
       "                        <td id=\"T_dd161_row14_col2\" class=\"data row14 col2\" >2.78%</td>\n",
       "                        <td id=\"T_dd161_row14_col3\" class=\"data row14 col3\" >3.34%</td>\n",
       "                        <td id=\"T_dd161_row14_col4\" class=\"data row14 col4\" >2.84%</td>\n",
       "                        <td id=\"T_dd161_row14_col5\" class=\"data row14 col5\" >2.84%</td>\n",
       "                        <td id=\"T_dd161_row14_col6\" class=\"data row14 col6\" >2.66%</td>\n",
       "                        <td id=\"T_dd161_row14_col7\" class=\"data row14 col7\" >2.86%</td>\n",
       "                        <td id=\"T_dd161_row14_col8\" class=\"data row14 col8\" >3.51%</td>\n",
       "                        <td id=\"T_dd161_row14_col9\" class=\"data row14 col9\" >2.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_dd161_row15_col0\" class=\"data row15 col0\" >6.59%</td>\n",
       "                        <td id=\"T_dd161_row15_col1\" class=\"data row15 col1\" >6.54%</td>\n",
       "                        <td id=\"T_dd161_row15_col2\" class=\"data row15 col2\" >6.84%</td>\n",
       "                        <td id=\"T_dd161_row15_col3\" class=\"data row15 col3\" >6.80%</td>\n",
       "                        <td id=\"T_dd161_row15_col4\" class=\"data row15 col4\" >7.08%</td>\n",
       "                        <td id=\"T_dd161_row15_col5\" class=\"data row15 col5\" >5.77%</td>\n",
       "                        <td id=\"T_dd161_row15_col6\" class=\"data row15 col6\" >7.42%</td>\n",
       "                        <td id=\"T_dd161_row15_col7\" class=\"data row15 col7\" >11.18%</td>\n",
       "                        <td id=\"T_dd161_row15_col8\" class=\"data row15 col8\" >9.70%</td>\n",
       "                        <td id=\"T_dd161_row15_col9\" class=\"data row15 col9\" >15.86%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_dd161_row16_col0\" class=\"data row16 col0\" >3.07%</td>\n",
       "                        <td id=\"T_dd161_row16_col1\" class=\"data row16 col1\" >2.85%</td>\n",
       "                        <td id=\"T_dd161_row16_col2\" class=\"data row16 col2\" >2.84%</td>\n",
       "                        <td id=\"T_dd161_row16_col3\" class=\"data row16 col3\" >2.89%</td>\n",
       "                        <td id=\"T_dd161_row16_col4\" class=\"data row16 col4\" >2.84%</td>\n",
       "                        <td id=\"T_dd161_row16_col5\" class=\"data row16 col5\" >3.18%</td>\n",
       "                        <td id=\"T_dd161_row16_col6\" class=\"data row16 col6\" >2.76%</td>\n",
       "                        <td id=\"T_dd161_row16_col7\" class=\"data row16 col7\" >2.44%</td>\n",
       "                        <td id=\"T_dd161_row16_col8\" class=\"data row16 col8\" >2.58%</td>\n",
       "                        <td id=\"T_dd161_row16_col9\" class=\"data row16 col9\" >2.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_dd161_row17_col0\" class=\"data row17 col0\" >6.68%</td>\n",
       "                        <td id=\"T_dd161_row17_col1\" class=\"data row17 col1\" >7.38%</td>\n",
       "                        <td id=\"T_dd161_row17_col2\" class=\"data row17 col2\" >7.43%</td>\n",
       "                        <td id=\"T_dd161_row17_col3\" class=\"data row17 col3\" >6.63%</td>\n",
       "                        <td id=\"T_dd161_row17_col4\" class=\"data row17 col4\" >7.15%</td>\n",
       "                        <td id=\"T_dd161_row17_col5\" class=\"data row17 col5\" >6.24%</td>\n",
       "                        <td id=\"T_dd161_row17_col6\" class=\"data row17 col6\" >7.40%</td>\n",
       "                        <td id=\"T_dd161_row17_col7\" class=\"data row17 col7\" >6.47%</td>\n",
       "                        <td id=\"T_dd161_row17_col8\" class=\"data row17 col8\" >5.17%</td>\n",
       "                        <td id=\"T_dd161_row17_col9\" class=\"data row17 col9\" >7.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_dd161_row18_col0\" class=\"data row18 col0\" >3.62%</td>\n",
       "                        <td id=\"T_dd161_row18_col1\" class=\"data row18 col1\" >3.53%</td>\n",
       "                        <td id=\"T_dd161_row18_col2\" class=\"data row18 col2\" >3.56%</td>\n",
       "                        <td id=\"T_dd161_row18_col3\" class=\"data row18 col3\" >3.24%</td>\n",
       "                        <td id=\"T_dd161_row18_col4\" class=\"data row18 col4\" >3.42%</td>\n",
       "                        <td id=\"T_dd161_row18_col5\" class=\"data row18 col5\" >3.64%</td>\n",
       "                        <td id=\"T_dd161_row18_col6\" class=\"data row18 col6\" >3.44%</td>\n",
       "                        <td id=\"T_dd161_row18_col7\" class=\"data row18 col7\" >2.64%</td>\n",
       "                        <td id=\"T_dd161_row18_col8\" class=\"data row18 col8\" >2.42%</td>\n",
       "                        <td id=\"T_dd161_row18_col9\" class=\"data row18 col9\" >2.43%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_dd161_row19_col0\" class=\"data row19 col0\" >4.71%</td>\n",
       "                        <td id=\"T_dd161_row19_col1\" class=\"data row19 col1\" >4.42%</td>\n",
       "                        <td id=\"T_dd161_row19_col2\" class=\"data row19 col2\" >4.62%</td>\n",
       "                        <td id=\"T_dd161_row19_col3\" class=\"data row19 col3\" >4.37%</td>\n",
       "                        <td id=\"T_dd161_row19_col4\" class=\"data row19 col4\" >4.61%</td>\n",
       "                        <td id=\"T_dd161_row19_col5\" class=\"data row19 col5\" >4.61%</td>\n",
       "                        <td id=\"T_dd161_row19_col6\" class=\"data row19 col6\" >4.75%</td>\n",
       "                        <td id=\"T_dd161_row19_col7\" class=\"data row19 col7\" >4.34%</td>\n",
       "                        <td id=\"T_dd161_row19_col8\" class=\"data row19 col8\" >4.20%</td>\n",
       "                        <td id=\"T_dd161_row19_col9\" class=\"data row19 col9\" >4.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_dd161_row20_col0\" class=\"data row20 col0\" >3.83%</td>\n",
       "                        <td id=\"T_dd161_row20_col1\" class=\"data row20 col1\" >4.28%</td>\n",
       "                        <td id=\"T_dd161_row20_col2\" class=\"data row20 col2\" >4.23%</td>\n",
       "                        <td id=\"T_dd161_row20_col3\" class=\"data row20 col3\" >4.67%</td>\n",
       "                        <td id=\"T_dd161_row20_col4\" class=\"data row20 col4\" >4.37%</td>\n",
       "                        <td id=\"T_dd161_row20_col5\" class=\"data row20 col5\" >4.66%</td>\n",
       "                        <td id=\"T_dd161_row20_col6\" class=\"data row20 col6\" >4.23%</td>\n",
       "                        <td id=\"T_dd161_row20_col7\" class=\"data row20 col7\" >4.65%</td>\n",
       "                        <td id=\"T_dd161_row20_col8\" class=\"data row20 col8\" >5.79%</td>\n",
       "                        <td id=\"T_dd161_row20_col9\" class=\"data row20 col9\" >3.94%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_dd161_row21_col0\" class=\"data row21 col0\" >3.34%</td>\n",
       "                        <td id=\"T_dd161_row21_col1\" class=\"data row21 col1\" >3.26%</td>\n",
       "                        <td id=\"T_dd161_row21_col2\" class=\"data row21 col2\" >3.07%</td>\n",
       "                        <td id=\"T_dd161_row21_col3\" class=\"data row21 col3\" >3.39%</td>\n",
       "                        <td id=\"T_dd161_row21_col4\" class=\"data row21 col4\" >3.11%</td>\n",
       "                        <td id=\"T_dd161_row21_col5\" class=\"data row21 col5\" >3.13%</td>\n",
       "                        <td id=\"T_dd161_row21_col6\" class=\"data row21 col6\" >2.98%</td>\n",
       "                        <td id=\"T_dd161_row21_col7\" class=\"data row21 col7\" >3.05%</td>\n",
       "                        <td id=\"T_dd161_row21_col8\" class=\"data row21 col8\" >3.37%</td>\n",
       "                        <td id=\"T_dd161_row21_col9\" class=\"data row21 col9\" >2.61%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_dd161_row22_col0\" class=\"data row22 col0\" >3.00%</td>\n",
       "                        <td id=\"T_dd161_row22_col1\" class=\"data row22 col1\" >2.87%</td>\n",
       "                        <td id=\"T_dd161_row22_col2\" class=\"data row22 col2\" >2.81%</td>\n",
       "                        <td id=\"T_dd161_row22_col3\" class=\"data row22 col3\" >2.67%</td>\n",
       "                        <td id=\"T_dd161_row22_col4\" class=\"data row22 col4\" >2.70%</td>\n",
       "                        <td id=\"T_dd161_row22_col5\" class=\"data row22 col5\" >3.08%</td>\n",
       "                        <td id=\"T_dd161_row22_col6\" class=\"data row22 col6\" >2.64%</td>\n",
       "                        <td id=\"T_dd161_row22_col7\" class=\"data row22 col7\" >2.01%</td>\n",
       "                        <td id=\"T_dd161_row22_col8\" class=\"data row22 col8\" >1.94%</td>\n",
       "                        <td id=\"T_dd161_row22_col9\" class=\"data row22 col9\" >1.73%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_dd161_row23_col0\" class=\"data row23 col0\" >5.44%</td>\n",
       "                        <td id=\"T_dd161_row23_col1\" class=\"data row23 col1\" >5.08%</td>\n",
       "                        <td id=\"T_dd161_row23_col2\" class=\"data row23 col2\" >5.39%</td>\n",
       "                        <td id=\"T_dd161_row23_col3\" class=\"data row23 col3\" >5.21%</td>\n",
       "                        <td id=\"T_dd161_row23_col4\" class=\"data row23 col4\" >5.49%</td>\n",
       "                        <td id=\"T_dd161_row23_col5\" class=\"data row23 col5\" >5.06%</td>\n",
       "                        <td id=\"T_dd161_row23_col6\" class=\"data row23 col6\" >5.75%</td>\n",
       "                        <td id=\"T_dd161_row23_col7\" class=\"data row23 col7\" >6.40%</td>\n",
       "                        <td id=\"T_dd161_row23_col8\" class=\"data row23 col8\" >6.18%</td>\n",
       "                        <td id=\"T_dd161_row23_col9\" class=\"data row23 col9\" >7.23%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd161_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_dd161_row24_col0\" class=\"data row24 col0\" >3.01%</td>\n",
       "                        <td id=\"T_dd161_row24_col1\" class=\"data row24 col1\" >2.79%</td>\n",
       "                        <td id=\"T_dd161_row24_col2\" class=\"data row24 col2\" >2.82%</td>\n",
       "                        <td id=\"T_dd161_row24_col3\" class=\"data row24 col3\" >2.87%</td>\n",
       "                        <td id=\"T_dd161_row24_col4\" class=\"data row24 col4\" >2.84%</td>\n",
       "                        <td id=\"T_dd161_row24_col5\" class=\"data row24 col5\" >3.33%</td>\n",
       "                        <td id=\"T_dd161_row24_col6\" class=\"data row24 col6\" >2.78%</td>\n",
       "                        <td id=\"T_dd161_row24_col7\" class=\"data row24 col7\" >2.52%</td>\n",
       "                        <td id=\"T_dd161_row24_col8\" class=\"data row24 col8\" >2.81%</td>\n",
       "                        <td id=\"T_dd161_row24_col9\" class=\"data row24 col9\" >2.13%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd2e55b29a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
